A logistic regression investigation of the relationship between the Learning Assistant model and failure rates in introductory STEM courses

"They Have Shown Me It Is Possible to Thrive within STEM": Incorporating Learning Assistants in General Chemistry Enhances Student Belonging and Confidence

Students often experience social and psychological barriers to success in General Chemistry, which is a key gateway to many students' science pathways. Learning assistants (LAs) have the potential to reduce these barriers and to strengthen students' sense of belonging in General Chemistry and STEM more broadly. Here, we used a 17-item Likert scale to determine whether incorporating LAs into General Chemistry I and II enhances students' sense of belonging in these courses. The incorporation of LAs into General Chemistry I had a significant positive effect and a medium to large effect size for students in all student groups examined: women and men; students in both racially and ethnically underrepresented and well-represented groups; first- and continuing-generation students. In General Chemistry II, similar results were observed for women and men; students in well-represented racial and ethnic groups; continuing-generation students. Further, we asked students to reflect on the impact that working with LAs had on their sense of belonging in STEM and confidence in talking about science. Sixty percent of students indicated that working with LAs had a positive impact on their STEM belonging, with five themes describing LA impacts: reducing isolation, serving as inspirational role models, providing mentoring, increasing opportunities for engagement and confidence building, and serving as accessible and approachable sources of support. Sixty-one percent of students also indicated that working with LAs increased their confidence in talking about science, with three themes emerging: fostering an environment with a lower risk of negative judgment, providing increased opportunities for feedback, and supporting students as they practiced their growing skills. Together, these results indicate that LAs can be an important means to reduce social and psychological barriers for students in gateway science courses, increasing their sense that they belong to the class and STEM more broadly.

Participation in a High-Structure General Chemistry Course Increases Student Sense of Belonging and Persistence to Organic Chemistry

A parallel series of general chemistry courses for Life Science Majors was created in an effort to support students and improve general chemistry outcomes. We created a two-quarter enhanced general chemistry course series that is not remedial, but instead implements several evidence-based teaching practices including Process Oriented Guided Inquiry Learning (POGIL), Peer-Led Team Learning (PLTL), and the Learning Assistant (LA) model. We found that students who took enhanced general chemistry had higher persistence to the subsequent first organic chemistry course, and performed equally well in the organic course compared to their peers who took standard general chemistry. Students in the first enhanced general chemistry course also reported significantly higher belonging, although we were unable to determine if increased belonging was associated with the increased persistence to organic chemistry. Rather we found that the positive association between taking the enhanced general chemistry course and persistence to organic chemistry was mediated by higher grades received in the enhanced general chemistry course. Our findings highlight the responsibility we have as educators to carefully consider the pedagogical practices we use, in addition to how we assign student grades.

Improving models for student retention and graduation using Markov chains

Graduation rates are a key measure of the long-term efficacy of academic interventions. However, challenges to using traditional estimates of graduation rates for underrepresented students include inherently small sample sizes and high data requirements. Here, we show that a Markov model increases confidence and reduces biases in estimated graduation rates for underrepresented minority and first-generation students. We use a Learning Assistant program to demonstrate the Markov model's strength for assessing program efficacy. We find that Learning Assistants in gateway science courses are associated with a 9% increase in the six-year graduation rate. These gains are larger for underrepresented minority (21%) and first-generation students (18%). Our results indicate that Learning Assistants can improve overall graduation rates and address inequalities in graduation rates for underrepresented students.

A Lesson from the Pandemic: Utilizing Digital Tools To Support Student Engagement during Instructional Assistant-Led Sessions

Student instructional assistants (IAs) are an integral part of most students' college experience in higher education. When properly trained, IAs can improve students' grades, engagement with course content, persistence, and retention. Recently, the COVID-19 pandemic forced the transition of nearly all instructional practices online. At the University of Alabama at Birmingham, IAs, including Biology Learning Assistants (BLAs), began hosting their instructional sessions virtually, outside of class time. The goals of these sessions were to reinforce fundamental concepts using active learning strategies and to address student questions by building a supportive learning community. In this article, we summarize the training and guidance we provided to the BLAs regarding how best to adapt digital educational tools to engage students during their virtual sessions. We recommend that institutions of higher education recognize the expansion of digital educational tools as an opportunity to increase the technological literacy and competence of their IAs to best serve their student body in this increasingly digital age of education.

"It made me feel like a bigger part of the STEM community": Incorporation of Learning Assistants Enhances Students' Sense of Belonging in a Large Introductory Biology Course

Large introductory science courses are a particularly important and challenging target for creating inclusive learning environments. In this study, we examined the impact of incorporating learning assistants (LAs) on the learning environment in an introductory biology course taught with two different structures: an in-person lecture with intermittent active-learning components and an online setting taught with a flipped instructional approach. Using a survey that measured sense of belonging in a single class, we found that students in sections with LAs reported greater sense of belonging than students in sections without LAs in both class structures. Further, student focus groups revealed that LAs promoted learning and engagement in the class by answering questions and providing clarity; allowing more use of active- and interactive-learning structures; and serving as accessible, approachable, and immediate sources of help. Student responses also indicated that LAs promoted a sense of belonging in science, technology, engineering, and mathematics (STEM) by decreasing feelings of isolation, serving as inspirational role models, clarifying progression through the STEM educational system, and helping students become more engaged and confident in their STEM-related knowledge and skills. These findings indicate that LAs can support multiple elements of inclusive STEM learning environments.

Shifts in learning assistants' self-determination due to COVID-19 disruptions in Calculus II course delivery

BACKGROUND

The Learning Assistant (LA) model with its subsequent support and training has evidenced significant gains for undergraduate STEM learning and persistence, especially in high-stakes courses like Calculus. Yet, when a swift and unexpected transition occurs from face-to-face to online, remote learning of the LA environment, it is unknown how LAs are able to maintain their motivation (competence, autonomy, and relatedness), adapt to these new challenges, and sustain their student-centered efforts. This study used Self-Determination Theory (SDT) to model theoretical aspects of LAs' motivations (persistence and performance) both before and after changes were made in delivery of a Calculus II course at Texas Tech University due to COVID-19 interruptions.

RESULTS

Analysis of weekly written reflections, a focus group session, and a post-course questionnaire of 13 Calculus II LAs throughout Spring semester of 2020 showed that LAs' reports of competence proportionally decreased when they transitioned online, which was followed by a moderate proportional increase in reports of autonomy (actions they took to adapt to distance instruction) and a dramatic proportional increase in reports of relatedness (to build structures for maintaining communication and building community with undergraduate students).

CONCLUSIONS

Relatedness emerged as the most salient factor from SDT to maintain LA self-determination due to the COVID-19 facilitated interruption to course delivery in a high-stakes undergraduate STEM course. Given that online learning continues during the pandemic and is likely to continue after, this research provides an understanding to how LAs responded to this event and the mounting importance of relatedness when LAs are working with undergraduate STEM learners. Programmatic recommendations are given for enhancing LA preparation including selecting LAs for autonomy and relatedness factors (in addition to competence), modeling mentoring for remote learners, and coaching in best practices for online instruction.

Ensemble based machine learning approach for prediction of glioma and multi-grade classification

Glioma is the most pernicious cancer of the nervous system, with histological grade influencing the survival of patients. Despite many studies on the multimodal treatment approach, survival time remains brief. In this study, a novel two-stage ensemble of an ensemble-type machine learning-based predictive framework for glioma detection and its histograde classification is proposed. In the proposed framework, five characteristics belonging to 135 subjects were considered: human telomerase reverse transcriptase (hTERT), chitinase-like protein (YKL-40), interleukin 6 (IL-6), tissue inhibitor of metalloproteinase-1 (TIMP-1) and neutrophil/lymphocyte ratio (NLR). These characteristics were examined using distinctive ensemble-based machine learning classifiers and combination strategies to develop a computer-aided diagnostic system for the non-invasive prediction of glioma cases and their grade. In the first stage, the analysis was conducted to classify glioma cases and control subjects. Machine learning approaches were applied in the second stage to classify the recognised glioma cases into three grades, from grade II, which has a good prognosis, to grade IV, which is also known as glioblastoma. All experiments were evaluated with a five-fold cross-validation method, and the classification results were analysed using different statistical parameters. The proposed approach obtained a high value of accuracy and other statistical parameters compared with other state-of-the-art machine learning classifiers. Therefore, the proposed framework can be utilised for designing other intervention strategies for the prediction of glioma cases and their grades.

Predicting Increase in Demand for Public Buses in University Students Daily Life Needs: Case Study Based on a City in Japan

Accessibility and economic sustainability of public bus services (PBS) have been in a continuous decline in Japan’s countryside. Rural cities also suffer from population transformation toward industrial centers experiencing rapid economic growth. In the present study, we reviewed the current demand status of PBS in Kitami, a rural city in Japan that hosts a national university. The investigation was performed by examining students’ daily lives using a survey to collect data representing a portion of the population. The objective was to predict the change in demand rate for PBS concerning the necessities of everyday life from the perspective of university students as potential users of PBS. Intuitively, decision-makers at every level display a distinct prejudice toward alternatives that intend to change the long-lasting status quo, hence in the question sequence, a two-step verification probe was used to reveal a person’s actual perceived opinion. Accordingly, the respondents’ initial demand rate for PBS was around 60%; however, this score increased to 71% in the secondary confirmation. Afterward, using machine learning-based prediction methods, we could predict this demand at over 90% of F-measure, with the most reliable and stable prediction method reaching 80% by other daily life indicators’ weight. Finally, we supplied thorough evidence for our approach’s usability by collecting and processing the data’s right set regarding this study’s objective. This method’s highlighted outcomes would help to reduce the local governments’ and relevant initiatives’ adaptability time to demands and improve decision-making flexibility.